Top-Down Multi-Person 3D Pose Estimation.
Early works focused on human-centric tasks by using a top-down manner without estimating individual depth [1]}, [2]}. Only a few works tackle the problem of camera-centric multi-person 3D pose estimation from a monocular RGB image or video. Moon et al. [3]} was the first to suggest locating a person’s root absolute depth by learning a correction factor of the area of 2D bounding box, while Lin et al. [4]} proposed a pose-aware human depth estimation network to address the problem of root joint localization for multi-person 3D pose estimation in the camera-space. Similarly, Veges et al. [5]} proposed to use two separate networks, pose estimator and depth estimator, to recover camera-centric multi-person 3D pose. To handle inherent depth ambiguity, Wang et al. [6]} proposed a novel hierarchical multi-person ordinal relation. Another type of work utilizes temporal information to recover 3D poses from a given video[7]}, [8]}. By applying a top-down scheme, the above method either directly regresses the absolute 3D depth from a cropped image, or it computes it based on a prior of the body size, ignoring global image contexts. Moreover, they suffer from high computational redundancy with an additional detection stage. By contrast, our methods can utilize global image contexts and enjoy low computational costs as they are less affected by the number of humans.
